Gault, B.: Full determination of 3D atomic position by combining APT & EM. Scientific Directions for Future TEM, Forschungszentrum Jülich, Jülich, Germany (2016)
Gault, B.; Katnagallu, S.: Atom probe microscopy: a new playground for big data analysis? Workshop Big-Data-Driven Materials Science, Ringberg Castle, Rottach, Germany (2016)
Gault, B.; De Geuser, F.: A perspective on the ion projection in field ion & atom probe microscopy. Atom Probe Tomography & Microscopy 2016, Gyeongju, South Korea (2016)
Raabe, D.; Choi, P.-P.; Gault, B.; Ponge, D.; Yao, M.; Herbig, M.: Segregation engineering for self-organized nanostructuring of materials - from atoms to properties? APT&M 2016 - Atom Probe Tomography & Microscopy 2016 (55th IFES) , Gyeongju, South Korea (2016)
Kuzmina, M.; Gault, B.; Herbig, M.; Ponge, D.; Sandlöbes, S.; Raabe, D.: From grains to atoms: ping-pong between experiment and simulation for understanding microstructure mechanisms. Res Metallica Symposium, Department of Materials Engineering, KU Leuven, Leuven, The Netherlands (2016)
Herbig, M.; Ponge, D.; Gault, B.; Borchers, C.; Raabe, D.: Segregation and phase transformation at dislocations during aging in a Fe-9%Mn steel studied by correlative TEM-atom probe tomography. MSE 2014, Darmstadt, Germany (2014)
Scientists of the Max-Planck-Institut für Eisenforschung pioneer new machine learning model for corrosion-resistant alloy design. Their results are now published in the journal Science Advances
The project’s goal is to synergize experimental phase transformations dynamics, observed via scanning transmission electron microscopy, with phase-field models that will enable us to learn the continuum description of complex material systems directly from experiment.
In order to prepare raw data from scanning transmission electron microscopy for analysis, pattern detection algorithms are developed that allow to identify automatically higher-order feature such as crystalline grains, lattice defects, etc. from atomically resolved measurements.
The general success of large language models (LLM) raises the question if they could be applied to accelerate materials science research and to discover novel sustainable materials. Especially, interdisciplinary research fields including materials science benefit from the LLMs capability to construct a tokenized vector representation of a large…
Crystal Plasticity (CP) modeling [1] is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in…